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Plant-soil interactions and acclimation to temperatureof microbial-mediated soil respiration may affectpredictions of soil CO2 efflux
J. Curiel Yuste • S. Ma • D. D. Baldocchi
Received: 8 January 2009 / Accepted: 20 September 2009 / Published online: 14 October 2009
� The Author(s) 2009. This article is published with open access at Springerlink.com
Abstract It is well known that microbial-mediated
soil respiration, the major source of CO2 from
terrestrial ecosystems, is sensitive to temperature.
Here, we hypothesize that some mechanisms, such as
acclimation of microbial respiration to temperature
and/or regulation by plant fresh C inputs of the
temperature sensitivity of decomposition of soil
organic matter (SOM), should be taken into account
to predict soil respiration correctly. Specifically, two
hypotheses were tested: (1) under warm conditions,
temperature sensitivity (Q10) and basal rates of
microbial-mediated soil respiration (Bs20, respiration
at a given temperature) would be primarily subjected
to presence/absence of plant fresh C inputs; and
(2) under cold conditions, where labile C depletion
occurred more slowly, microbial-mediated soil respi-
ration could adjust its optimal temperatures to colder
temperatures (acclimation), resulting in a net increase
of respiration rates for a given temperature (Bs20). For
this purpose, intact soil cores from an oak savanna
ecosystem were incubated with sufficient water sup-
ply at two contrasting temperatures (10 and 30�C)
during 140 days. To study temperature sensitivity of
soil respiration, short-term temperature cycles (from 5
to 40�C at 8 h steps) were applied periodically to the
soils. Our results confirmed both hypotheses. Under
warm conditions ANCOVA and likelihood ratio tests
confirmed that both Q10 and Bs20 decreased signifi-
cantly during the incubation. Further addition of
glucose at the end of the incubation period increased
Bs20 and Q10 to initial values. The observed decrease
in temperature sensitivity (Q10) in absence of labile C
disagrees with the broadly accepted fact that temper-
ature sensitivity of the process increases as quality of
the substrate decreases. Our experiment also shows
that after 2 months of incubation cold-incubated soils
doubled the rates of respiration at cold temperatures
causing a strong increase in basal respiration rates
(Bs20). This suggest that microbial community may
have up-regulated their metabolism at cold conditions
(cold-acclimation), which also disagrees with most
observations to date. The manuscript discusses those
two apparent contradictions: the decrease in temper-
ature sensitivity in absence of labile C and the
increase in microbial-mediated soil respiration rates
at cold temperatures. While this is only a case study,
the trends observed could open the controversy over
the validity of current soil respiration models.
Keywords Soil respiration � Carbon cycle
modeling � Ecosystem ecology � Climate change
J. Curiel Yuste � S. Ma � D. D. Baldocchi
Department of Environmental Science, Policy, and
Management, University of California at Berkeley,
137 Mulford Hall, Berkeley, CA 94720, USA
J. Curiel Yuste (&)
Ecophysiology and Global Change Unit CREAF-CEAB-
CSIC, CREAF (Center for Ecological Research and
Forestry Applications), Edifici C, Universitat Autonoma
de Barcelona, 08193 Bellaterra, Spain
e-mail: [email protected]
123
Biogeochemistry (2010) 98:127–138
DOI 10.1007/s10533-009-9381-1
Introduction
How climate change will perturb the soil organic
carbon (C) reservoir remains a controversy. Micro-
bial-mediated soil respiration, resulting from the
decomposition of soil organic matter (SOM) and its
transport through the soil, ultimately contributes to an
important portion of soil CO2 efflux (Hanson et al.
2000; Bond-Lamberty et al. 2004; Tang and Baldoc-
chi 2005; Hahn et al. 2006). This fact highlights the
need to understand physiological mechanisms of
microbial metabolism. Biologists have long used
exponential functions to describe the temperature
dependence of soil respiration, a concept originated
in the 19th century physical-chemistry models of
Arrhenius (1889) and Van’t Hoff (1898). Current
models predict that global warming will increase the
net CO2 emissions from terrestrial ecosystems due to
the strong sensitivity of heterotrophic respiration to
temperature (Cox et al. 2000; Friedlingstein et al.
2003; Rustad et al. 2001). However, those models
also highlight the large uncertainties associated to
predicted soil emissions component (Cox et al. 2000;
Cramer et al. 2001; Meir et al. 2006) which are
primarily due to the limited, but growing, knowledge
of the mechanisms underlying soil respiration
(Davidson and Janssens 2006; Davidson et al. 2006).
One of these mechanisms is acclimation, which is
the homeostatic adjustment of respiration rates in
organisms to compensate for a change in temperature
(Atkin et al. 2000). We know that plants can adjust
physiologically, by increasing their metabolic activity
at low temperatures and/or down-regulating their
metabolic activity at warm conditions (Berry and
Bjorkman 1980; Atkin and Tjoelker 2003). Concern-
ing soils, it is not yet clear whether microbial-
mediated soil respiration acclimates to climatic
changes and how this acclimation would affect future
predictions of terrestrial CO2 emissions. Recent
studies have criticized first order kinetics models
because they do not reflect the capacity of microbial
communities to functionally acclimate to previously
unknown environmental conditions (Schimel 1995;
Stark and Firestone 1996; Schimel and Gulledge
1998; Balser and Firestone 2001; Hawkes et al.
2005), as has been shown in a number of studies
(Zogg et al. 1997; Balser and Firestone 2004;
Waldrop and Firestone 2006).
At the ecosystem level, several studies have
observed a decrease in soil respiration in association
with metabolic down-regulation at warmer tempera-
tures (Luo et al. 2001; Stromgren and Linder 2002;
Giardina and Ryan 2000; Misson et al. 2007).
However a number of studies have attributed this
apparent acclimation to other factors; as temperature
increases the imbalance arises between the supply
(photosynthesis) and the utilization (respiration) of
the most labile C fraction, product of plant activity
(Rustad et al. 2001; Kirschbaum 1995, 2004; Gu et al.
2004; Eliasson et al. 2005; Davidson et al. 2006;
Hartley et al. 2007). Recently synthesized sugars
contribute substantially to soil respiration (Hogberg
et al. 2001; Tang et al. 2005) and provide microbes
with the energy to support decomposition of SOM
(priming) (Kuzyakov and Cheng 2001, 2004; Fon-
taine et al. 2003, 2007). Moreover, because our
perception of temperature sensitivity of decomposi-
tion could be strongly affected by substrate supply at
the enzymatic-process level (Davidson and Janssens
2006), it is important to distinguish apparent
(observed) from intrinsic (real) temperature sensitiv-
ity of soil respiration.
Following these open controversies, we hypothesize
that factors not taken into account by current models,
such as thermal acclimation of microbial-mediated soil
respiration and/or the size of the labile fraction of soil
organic matter (SOM) at a given time, may strongly
affect soil respiration and its response to temperature.
Specifically, we here hypothesize that (1) under warm
conditions Q10 (the increase in respiration for every
10�C) and Bs20 (The basal respiration rate for a given
temperature) of microbial-mediated soil respiration will
be subjected to modulations by plant fresh C inputs, i.e.
the lack of plant fresh C inputs will affect respiration and
its response to temperature negatively; and (2) under
cold conditions and more slowly depletion of labile C,
soil respiration will increase at lower temperatures with
time (acclimation), affecting also Q10 and the Bs20, i.e.
as observed in plants, microbial community may adjust
their metabolism to new temperatures. For this aim, soils
were incubated at two contrasting temperatures, above
and below the mean annual temperature, respectively,
for 140 days. To investigate possible patterns of accli-
mation to temperature during the incubation short-term
temperature changes were applied periodically to the
same soils.
128 Biogeochemistry (2010) 98:127–138
123
Materials and methods
Site description
Intact soils cores were sampled from an oak savanna
field site (Tonzi Ranch), located at 38.4311�N,
120.966�W near Ione, Ca. The altitude of the site is
177 m and the terrain is relatively flat. The woodland
overstory consists of scattered blue oak trees (Quercus
douglasii). Also registered in the site survey were
occasional grey pine trees (Pinus sabiniana). The
understory landscape has been managed, as the local
rancher has removed brush and the cattle graze the
grass and herbs. The understorey consists of exotic
annual grasses and herbs; the species include Brac-
hypodium distachyon, Hypochaeris glabra, Bromus
madritensis, and Cynosurus echinatus. Deciduous
blue oaks (Quercus douglasii) dominate the savanna
site with 144 stem per hectare in a 200 by 200 m
sampling plot. Their average height is 9.41 ± 4.33 m,
and their mean basal area is 0.074 ± 0.0869 m2
(Chen et al. 2006). The mean annual temperature is
16.3�C, and 559 mm of precipitation fall per year, as
determined from over 30 years of data from a nearby
weather station at Ione, California.
Experimental design
We decided to sample at peak of biomass and
photosynthetic activity (Xu and Baldocchi 2004)
when soil metabolic activity is typically at its highest
values (Tang and Baldocchi 2005; Curiel Yuste et al.
2007). Intact soil cores were collected on 15 April
2006. By this date, grasses were growing and tree
leaves just came out, and mean soil temperature was
*15�C. Thirty undisturbed soil cores of 80 cm3
(4 9 4 9 5 cm) were collected from the upper part of
the soil profile (0–5 cm) with a soil sampler contain-
ing a stainless-steel cylinder within. Soil cores were
kept in their stainless steel container to maintain the
bulk density and known volume (80 cm3).
Soil cores were collected at 10 sub-locations, five
sub-locations randomly chosen in the proximity of a
tree (blue oak) and the other five in sub-locations
randomly chosen at least 20 m away from the nearest
tree. We did not find important differences in the
respiration and its response to temperature (Q10) of
soils collected in open and under tree areas (data not
shown). Therefore, data from both areas were pooled
together. Within each sub-location, three pseudo-
replicates were taken carefully one next to each-other
to avoid large ‘environmental’ differences (e.g. plant
cover, texture) among them. From these soil cores,
one was used for incubation at 10�C (to test ‘cold-
acclimation’), one was used for incubation at 30�C
(to test ‘warm-acclimation’) and the last one was
used for analysis (soil water content, soil water
potential, total carbon and nitrogen). Therefore, from
a total number of 30 cores, ten cores were maintained
at 30�C, other ten were maintained at 10�C and the
last ten soil cores were used for laboratory analytical
studies. Given the number of replicates (ten per
treatment), and the methodology used, we can assume
that soil cores at cold and warm treatments held the
same biogeochemical properties, at the initiation of
the incubation treatment.
Because the mean annual temperature Ta
� �of the
ecosystem is around 16�C we used these two con-
trasting temperatures to bound the climatic average.
At the time of sample collection, soils were suffi-
ciently moist (Table 1). To avoid water limitations
during the incubation period, soil cores were placed
within 473 ml glass Mason jars equipped with a
continuously water-saturated sponge to keep the soil
moistened by capillary action. By continuously main-
taining the sponge water-saturated soil moisture was
maintained near field capacity during the incubation.
To assess the temperature sensitivity of soil
decomposition at different stages of the incubation,
four temperature cycles were performed. During a
Table 1 Physical and biochemical properties of the oak
savanna soil under study for warm (warm) and cold (cold)
incubated soil cores
Warm Cold
Dry weight (g) 132(12) 131(11)
Soil moisture (g/g) 33.5(5.8) 33.8(5.7)
N content (g) 0.25(0.10) 0.25(0.05)
C content (g) 2.7(0.8) 2.7(0.7)
Bulk density (g/cm3) 1.5(0.1) 1.4(0.1)
Cf(g) 0.14(0.04) 0.14(0.04)
kf(d-1) 0.05 (0.03) 0.01
ks(d-1) 1.9 9 10-3(1.0 9 10-4) 4.7 9 10-4
Cf = fast C fraction of soil C; kf = rate constants of fast C
fraction; ks = rate constants of slow C fraction. Numbers in
bracket correspond to the standard deviation
Biogeochemistry (2010) 98:127–138 129
123
cycle, temperatures were increased from 5 to 40�C
and then decreased again to 5�C at temperature steps
of 5, 20, 30 and 40�C. To ensure temperature
equilibration within the soil cores, soil CO2 efflux
was measured after *6–7 h exposure to the new
temperature. To ensure the reliability of the mea-
surements, soil CO2 efflux was measured two
consecutive times at each soil sample and an average
of the two measurements was used for later calcula-
tions. Therefore, each temperature cycle lasted *36–
42 h. These cycles were performed at 10, 17, 61 and
140 days after the incubation started.
To test the first hypothesis, glucose––a source of
labile C––was added to soils depleted of labile C
(warm incubated during 140 days). At the end of the
incubation period (day 140), five of the soils previ-
ously incubated at 30�C received 1 ml of a glucose
solution (100 mg ml-1). To ensure that the glucose
pool did not become exhausted during the temperature
perturbation cycle, the quantity added was based on
the amount of CO2 depleted during the first temper-
ature cycle. The glucose was injected using a syringe
in several locations within the cores to spread the
glucose evenly. Two temperature cycles, as described
above, were performed before ((-) glucose) and after
((?) glucose) the addition of the solution.
Soil analyses
Bulk density, soil moisture as well as soil carbon (C)
and nitrogen (N) concentrations were measured at time
0 using the soil cores spared for analysis. Soil moisture
was estimated gravimetrically, by drying the samples
during 48 h at 75�C. By estimating the dry weight of
the soil cores of known volume (80 cm3) we estimated
the bulk density of the sample. Results of moisture and
C content of those soil cores were used to calculate the
soil moisture and C content of the incubated soils
assuming that the proportion (g/g) of water and C were
similar for both sets of soils. For more information
about soil analyses and methods see Curiel Yuste et al.
(2007). Results are presented in Table 1.
Calculation of respiration rates
We measured soil CO2 efflux using a dynamic flow-
through system which operated under closed and
non-steady state conditions. Concentrations of CO2
in the system were measured with a Li-Cor 6262
infrared gas analyzer (Li-Cor, Lincoln, NE). For
more information about the system, see Curiel Yuste
et al. (2007). Soil respiration was calculated from
the linear increase of CO2 concentration within the
system with time (normally 40 s time interval)
(Livingston and Hutchinson 1995; Curiel Yuste
et al. 2007):
Fa ¼ðDCO2 � aÞ � Vs
t � Að1Þ
where Fa is the total soil CO2 evolved from the soil
sample during the sampling interval (lmol m-2 s-1),
DCO2 is the change in CO2 concentration (lmol m-3)
within the system during the sampling interval, t is the
sample interval (s), a is the intercept of the linear
function of the relationship between CO2 concentra-
tion and time, Vs is the volume of the system (l) and
A is the area of the upper part of the core (1.81 9
10-3 m2). Volume of the system (Vs) was estimated
as 0.57 l (Curiel Yuste et al. 2007).
Sensitivity to temperature of soil CO2 efflux
To assess the sensitivity to temperature of respiration,
we used the Q10 function. Q10 represents the factor by
which respiration is multiplied when temperature
increases by 10�C:
Fa ¼ Bs20 QT�20
10
10 ð2Þ
Where Fa is the measured soil CO2 efflux on area
basis (lmol m-2 s-1), Bs20 is the basal respiration
rate at 20�C and T is the temperature of soil at
measurement time. The basal respiration rate was
evaluated at 20�C, a temperature in between both
treatments. We fitted this exponential function at each
temperature cycle for each soil core individually
(warm- and cold-acclimated soil cores) and for the
whole temperature range between 5 and 40�C.
Calculation of soil carbon pools
There are several equations that can be used to infer
the labile and recalcitrant C pools in soils. In
principle, these are based on the changes in the slope
of the C mineralization along the incubation period
(Sleutel et al. 2005; Katterer et al. 1998; Townsend
et al. 1997). The method used in this study assumes
that the C mineralized initially had a fast turnover and
130 Biogeochemistry (2010) 98:127–138
123
is labile (fast pool), while the remaining fraction has a
slow turnover and is recalcitrant (slow pool) (Town-
send et al. 1997). We used a two pool first-order
kinetics model used in prior works (Breland 1994;
Franzluebbers et al. 1994; Bernal et al. 1998):
CcumðtÞ ¼ Cf ½1� e�ðkf tÞ� þ (Ctotal � CfÞ½1� e�ðkstÞ�ð3Þ
In Eq. 3 Ccum(t) is the cumulative mineralized C at a
certain time of the incubation, expressed as Fa
(gC m-2), kf and ks are the decomposition rate of
the fast and slow C pool (d-1), Cf is the carbon
content of the fast pool and Ctotal the calculated soil C
(Table 2) (Kg C m-2). Ccum was calculated for each
day using linear interpolation between days where
soil respiration was measured. This model gave the
best fits out of the three different kinetics models
compared in Curiel Yuste et al. (2007). The fit was
improved by constraining the size of the slow pool
(assumed Cs as Ctotal - Cf) (McLauchlan and Hobbie
2004). It should be pointed out that the ‘slow pool’ in
this study refers to a mixture of soil C pools of very
different turnover times, probably from weeks up to
centuries or millennia (Trumbore 2000).
We wanted to test whether the differences in
respiration rates of cold- and warm-incubated soils
could be explained by differences in temperature. To
this end we modeled temporal evolution of soil
respiration based on observed temporal evolution of
soil respiration and temperature sensitivity. Soil respi-
ration in cold-incubated soils did not follow an
exponential decay shape (Fig. 1), and therefore the
fits and coefficients generated by Eq. 3, when fitted to
cold-incubated respiration rates, were not meaningful
(data not shown). We, therefore, assumed that: (1)
initial values of labile C(Cf) in cold-incubated soils
were the same as in warm-incubated soils; (2) decom-
position rate were sensitive to temperature with
sensitivity equal to a Q10 of 2 (standard value of
Q10). We therefore used the decomposition rate (kf and
ks) obtained from fitting warm-incubated soil cores
(Eq. 3) and a Q10 function (similar to Eq. 2) to model
the decomposition rate of labile and recalcitrant pools
of the cold incubated soil cores:.
kwi30 ¼ kci10 QT�10
10
10 ð4Þ
Where kwi30 is the decomposition rate of either the
fast (i = f) or the slow (i = s) C pools obtained from Ta
ble
2L
eft
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8[
0.0
01
Biogeochemistry (2010) 98:127–138 131
123
the fit of Eq. 3 to respiration rates of warm-incubated
soils (at 30�C), kci10 is the basal decomposition rate at
a temperature of 10�C, which coincided with cold-
incubation temperature. This basal decomposition
rate was therefore used as the decomposition rate of
the fast (i = f) or the slow (i = s) C pools for the
cold-incubated soils. The value of Q10 was fixed at 2
and the value of T was 30.
Statistical analyses
Analysis of co-variance was used to test significant
differences in Bs20 and Q10 between and within both
temperature treatments (i.e. warm 30�C and cold
10�C). We carried out a null model likelihood ratio
test for the significance of Q10’s. We specifically
tested the null hypotheses of no change in Q10 during
the incubation (H0 = dQ10 = 0). For each treatment
(warm or cold), the null model was fitted with all of
available samples, and a sub-model was also fitted for
each incubation time. A v2 value was calculated with
the logarithmic likelihood from the null model and
the logarithmic likelihood from the model fitted at
each incubation time. This statistic has an asymptotic
v2-distribution with q-1 degrees of freedom, where q
is the effective number of covariance parameters
(those not estimated to be on a boundary constraint).
If the probability [v2 is \0.05, the null model is
rejected. In other words, differences in parameters of
the models fitted by incubation time are significant.
Likelihood ratio test was performed by using the
MIXED procedure of the standard statistical software
package SAS (Version 9.1, SAS Institute Inc., Cary,
NC, USA). Analysis of co-variance in STATISTICA,
and were tested against a significance level of 0.05.
Results
No biochemical differences between both warm and
cold incubated cores were found at the beginning of
the incubation (Table 1). Despite the similar initial
substrate quantity and quality of both sets of soils
(Table 1) respiration rates differed significantly
between temperature treatments (10 or 30�C)
(Fig. 1). Initial respiration rates of the warm-incu-
bated soil cores were almost five times higher than
those of the cold-incubated soil cores (Fig. 1a). Soil
respiration decreased exponentially under warm con-
ditions during the incubation period (Fig. 1a). Under
warm temperatures respiration was highest when
remaining Cf was highest (Fig. 1a, b). Simulated
depletion of labile C, calculated from the coefficients
obtained from Eq. 3 (warm incubations) and Eqs. 3
and 4 (cold incubations) also indicates that labile C
pool was probably depleted faster in warm soils
(Fig. 1b). This is because kf, the decomposition rate
of this fast C pool, was much higher for warm than
for cold-incubated soils (Table 1).
Evolution of respiration rates under warm condi-
tions were well explained by an exponential function
(Fig. 2a). Under cold-conditions initial respiration
rates were well explained by the decomposition rate
obtained from warm incubated soils (kwf and kws,
respectively) and corrected for temperature (Eq. 4)
(Fig. 2b). After 2 months of incubation, however,
respiration rates at 10�C increased and were best
explained by applying the k coefficients obtained
under warm-incubations (kwf and kws) (dotted line
Fig. 2b).
Study of the short-term temperature response
of microbial-mediated respiration rates indicates a
0
4
8
12
16(a)
Time (days)
Warm incubated
Cold incubated
SO
M d
ecom
posi
tion
rate
(mg
C d
ay-1)
0 20 40 60 80 100 120 1400.00
0.04
0.08
0.12
(b) Warm incubated
Cold incubated
Rem
aini
ng la
bile
C (
g)
Fig. 1 a Microbial-mediated soil respiration for two different
incubation temperatures, warm and cold, as a function of time.
Error bars represent the standard error of the mean; b modeled
available labile C fraction for the warm and the cold treatment
of the study
132 Biogeochemistry (2010) 98:127–138
123
general decrease in basal respiration rates (Bs20) and
the sensitivity to temperature (Q10) with time
(Table 2, Fig. 3). Likelihood ratio test of comparing
the Q10 constant vs the Q10 varying model was
statistically significant for both the warm and the cold
treatment (data not shown), which confirmed the
results obtained in Table 2. Ancova analyses with
‘‘treatment’’ (cold-warm) and ‘‘time’’ as independent
variables indicated strong relation between ‘‘treat-
ment’’ and Bs20 (Bs20 was significantly higher in cold
than in warm-incubated soils) and between ‘‘time’’
and Q10 (Q10 decreased significantly with time).
Within treatment, there was a strong effect of time
over both Bs20 and Q10 (Table 2). Bs20 increased
significantly in the cold treatment (95% confidence
interval) while in the warm treatment Bs20 decreased
significantly (P \ 0.0001). Intercept of the linear fit
between ‘‘time’’ and Bs20 and Q10 coefficients further
shows that temperature sensitivity (Q10) at the
beginning of the incubation were equal (1.9) for both
set of soils, but Bs20 was more than double in the
warm- than in the cold-incubated set of soils. The Q10
coefficient decreased significantly with time at both
treatments (slope of the linear fit between ‘‘time’’ and
Q10 significantly different from 0), at a 95% confi-
dence interval (Table 2). The decrease was more
pronounced under cold- than under warm incubated
soils (Table 2) but as said above, likelihood ratio test
confirmed that the decrease was statistically signif-
icant for both the warm and the cold treatment.
Glucose, the most common plant exudate (Grayston
et al. 1996), was added to Cf-depleted soils (incubated
during 140 days at 30�C) to study the effect in soil
respiration and its sensitivity to temperature (Fig. 4).
Addition of this energy-rich, nutrient-poor substrate
caused a significant increase on Bs20 and Q10 to values
similar to initial ones (compare Figs. 3a–4).
Discussion
In the studied soils, values of the easily decompos-
able pool (Cf) (Table 1) were in the higher range of
that observed in Rey and Jarvis (2006). This easily
decomposable pool has been correlated to plant
activity (Curiel Yuste et al. 2007), which was at its
peak during the sampling date (see Materials and
Methods). Active plants are continuously releasing
organic material to soil in the form of easily
decomposable substrate (exudates) such as simple
sugars, amino acids and organic acids (Lynch and
Whipps 1990; Grayston et al. 1996; Gleixner et al.
2005; Norton and Firestone 1991). It is therefore
likely that the relatively large size of labile C pool in
the studied soils could be partially attributed to the
peak in plant activity at the sampling time.
Microbial-mediated soil respiration occurred faster
at higher temperatures (Fig. 1a), as it has been shown
in multiple experiments (e.g. Reichstein et al. 2000;
Dalias et al. 2001). Under warm conditions, micro-
bial-mediated respiration rates decreased exponen-
tially in a very predictable way (Figs. 1a, 2a).
Temporal evolution of microbial-mediated soil res-
piration could therefore be explained by first order
kinetics: respiration was proportional to the amount
0
2
4
6
8
10
12
14
16
Time (days)
(a)
SO
M d
ecom
posi
tion
rate
s (m
g C
day
-1)
0 20 40 60 80 100 120 1400
2
4
6
8
10
12
14 (b)
Two pool model
Modified model
Data
Fig. 2 a Modeled and measured microbial-mediated soil
respiration for warm incubated soil cores as a function of
time; b Modeled and measured microbial respiration for cold
incubated soil cores as a function of time. Symbols represent
observed respiration rates. Solid line in a represents modeled
rates of soil respiration using Eq. 3. Solid line in b represents
modeled rates of soil respiration of cold-incubated soils, using
the k values obtained for warm soils (Eq. 3) corrected for
temperature (Eq. 4). Dotted line in b represents the results of
modeling cold incubation respiration rates using the warm
incubated k values, not corrected by temperature. Error bars
represent the standard error of the mean
Biogeochemistry (2010) 98:127–138 133
123
of a discrete number of C pools (two in the study
case) and the decomposition rate of each C pool,
which was temperature dependent (Fig. 2, Table 1).
Respiration rates for a given temperature decreased
gradually under the warm conditions of the incuba-
tion (Table 2, Fig. 3), which was reflected in
decrease in temperature sensitivity (Q10) and basal
respiration rates (Bs20) of warm incubated soils
(Fig. 3). At the end of the incubation, both Q10 and
Bs20 were significantly lower than at the beginning of
the incubation (Table 2). It is a well known fact that
respiration rates for a given temperature decreases as
labile C fraction also decreases. However, the
decrease in Q10 in absence or shortage of labile C
disagrees with theory (e.g. Bosatta and Agren 1998),
which states that temperature sensitivity of respira-
tion should increase as the quality of SOM decreases.
The increase in Bs20 and Q10 to values closer to initial
values after addition of glucose––the most common
plant exudates (Grayston et al. 1996)––(compare
Figs. 3a–4) further confirms the idea of higher Q10 in
presence of labile C.
A number of observational ‘deviations’ from the
kinetic theory (Liski et al. 1999; Luo et al. 2001; Fang
et al. 2005) suggest, however, that the complexity of the
process transcend a single theory (Davidson and
Janssens 2006). Physical or biochemical accessibility
to substrate by soil enzymes (Davidson et al. 2006;
Davidson and Janssens 2006) may strongly affect the
response to temperature of microbial decomposition of
SOM and obscure the intrinsic temperature sensitivity
of less labile fractions of SOM. Under the laboratory
conditions of this experiment and after 4 months of
incubation, the negative effect on Q10 caused by
depletion of labile C and subsequent limitation in
substrate accessibility to microbes and exo-enzymes
was probably stronger than the positive effect on Q10
derived from decomposition of more recalcitrant SOM.
Therefore we conclude that in natural ecosystems
0
2
4
6
8
10
12
14
(a) (b)
(d)(c)
Warm. Q 10 1.8(0.05); Bs 20 3.7 (0.04)Cold. Q10 1.7(0.1); Bs20 3.8(0.3)
10 days
Warm
Cold
SO
M d
ecom
posi
tion
rate
s (µ
mol
CO
2 m
-2 s
-1)
Warm. Q 10 1.7(0.03); Bs 20 2.6 (0.04)Cold. Q 10 1.8 (0.1); Bs 20 3.0 (0.2)
17 days
5 10 15 20 25 30 35 40 450
2
4
6
8
10
12
Warm. Q10 1.6 (0.03); Bs 20 1.7 (0.1)Cold. Q10 1.3 (0.2); Bs20 3.3 (0.2)
60 days
Temperature (°C)
5 10 15 20 25 30 35 40 45
Warm. Q10 1.5(0.1); Bs 20 1.4(0.2)Cold. Q10 1.2 (0.1); Bs 20 5.2 (1.0)
140 days
Fig. 3 Short term
temperature response
curves of microbial-
mediated soil respiration
rates at warm and cold-
incubated soils at different
days after incubation began
10 (a), 17 (b), 60 (c) and
140 days (d). Error bars
represent the standard error
of the mean. All P-values
obtained from the
regressions were below the
0.005 significant level
5 1 0 1 5 20 2 5 3 0 3 5 4 0 0
2
4
6
8
10
12
Temperature (°C)
Soi
l CO
2 ef
flux
(µm
ol C
O2
m-2 s
-1) (-)Glucose; Q10=1.17(0.16);Bs20=2.3(0.4)
(+)Glucose; Q10=1.95(0.16);Bs20=3.0(0.4)
Fig. 4 Short term temperature response curves of microbial-
mediated soil respiration rates of warm-incubated soils before
((-) Glucose) and after ((?) Glucose) addition of glucose
solution. Error bars represent the standard error of the mean.
Q10 values and standard error of the fit (parenthesis) are also
provided
134 Biogeochemistry (2010) 98:127–138
123
temperature sensitivity of microbial-mediated soil
respiration would be primarily subjected to changes in
the accessibility to an easily decomposable C pool
rather than to changes in the intrinsic temperature
sensitivity of the C pool being decomposed.
On the other hand, the increase in Bs20 and the
decrease in Q10 after 60 days exposure to cold
(Figs. 1a–3d) were unexpectedly pronounced. Sub-
strate depletion probably occurred more slowly in
these soils, as suggested by simulated depletion of
labile C (Fig. 1b). The fairly constant Bs20 before day
60 and the increase in Bs20 after day 60 furthermore
support this fact. The increase in Bs20 and the
decrease in Q10 after 2 months of incubations were
mainly caused by a strong increase in respiration
rates at colder temperatures (Fig. 3) which could not
be explained by a first-order kinetic model corrected
for temperature (Fig. 2b). It must be pointed out that
soils in this ecosystem rarely experienced cold
temperatures. Average temperature at 2 cm depth in
the mineral soil, 7 days prior to sample collection,
was 15�C. Mean annual temperature at this depth of
soil profile was 19�C and temperatures similar to
those of the incubation (10�C) were only reached
during the first month of 2005 (*2 months and a half
before sampling). It is well known that microbes may
experience physiological changes to become tolerant
to cold conditions, e.g. maintenance of membrane
fluidity or synthesis of cold-tolerant enzymes (Mar-
gesin et al. 2007). Moreover, microbial community
structure changes under contrasting temperatures
regimes, favoring microbes better adapted to the
new temperatures (Zogg et al. 1997; Pettersson and
Baath 2003; Waldrop and Firestone 2004, 2006).
Therefore it is possible that soil microbial community
under cold conditions may have developed mecha-
nisms of acclimation to these sub-optimal tempera-
tures (cold-acclimation).
However, this observed transient doubling of
respiration rates is just a singularity that disagrees
with most of the laboratory observations at low
temperatures reported to date (Reichstein et al. 2000;
Dalias et al. 2001; Rey and Jarvis 2006). We
nonetheless contend that the response observed in
this study cannot be fully compared with previous
observations where initial physical and biological
conditions of soil were strongly modified by soil
mixing, sieving, rewetting and/or oven drying. Manip-
ulations such as sieving breaks up aggregates and
makes available previously protected SOM, which
increase initial respiration rates, with respect to intact
cores (Hartley et al. 2007). Strong changes in soil
water content (oven drying and rewetting) also affect
microbial community composition (Lipson et al.
2002; Fierer et al. 2003), probably favoring opportu-
nistic species over the endemic community (Allison
2005). This therefore suggests that other experiments
should be similarly designed to refute/support our
results.
Our experimental design therefore shows that: (1)
under warm conditions, fast depletion of an easily
accessible C pool affected negatively to both micro-
bial-mediated soil respiration and its temperature
sensitivity; and (2) microbial community, in this
experiment, respired more for a given temperature
with time (cold-acclimation). Our results open the
controversy of whether soil respiration in a changing
climate could be satisfactorily explained using simple
first-order kinetics and fixed values of temperature
sensitivity of soil respiration.
Conceptual remarks
Based on our results, Fig. 5 illustrates an example of
how our perception of microbial-mediated soil respi-
ration at a temperature reference (BsTref) and its
temperature sensitivity (Q10) could be affected by
environmental factors.
Under no water limitations, the temperature
response curve of soil respiration would be primarily
controlled by presence/absence of labile C carbon
(Fig. 5a), which in natural ecosystems is provided by
plants. This will affect the perception of BsTref and
Q10 on a fixed temperature range. Contrary to theory,
this suggests that the negative effect on Q10 caused by
depletion of an easily accessible C pool was probably
stronger than the positive effect on Q10 derived from
decomposition of more recalcitrant SOM. For the
purpose of this study, these results suggest that
models should take into account the effect of plant-
derived fresh C inputs to correctly understand
temperature sensitivity of soil respiration.
When limitations in fresh labile C were not an
issue, which was the case in the cold-incubated soils,
microbial communities could increase their respiration
rates at low temperatures (Fig. 5b). These adjustments
resulted in strong changes in BsTref and Q10 for a given
temperature range. While current models allocate fixed
Biogeochemistry (2010) 98:127–138 135
123
Q10’s and decomposition rate for a given SOM pool,
we here show that microbial acclimation to cold
temperatures may affect both, respiration rates for a
given temperature and Q10 for a given temperature
range. Future predictions of soil CO2 efflux can thus
not be satisfactorily predicted if we do not understand
patterns of microbial community acclimation to
changing temperatures.
Acknowledgment This research was supported by the Kearney
Soil Science Foundation and the US Department of Energy’s
Terrestrial Carbon Program, grant No. DE-FG03-00ER63013.
These sites are members of the AmeriFlux and Fluxnet networks.
J Curiel Yuste received a Marie Curie Intra-European Fellowship
(EIF) from de European Union for project MICROCARB (FP6-
2005-Mobility-5 # 041409-MICROCARB) while conducting
this research. We thank Ted Hehn for technical assistance during
this experiment and Mr. Russell Tonzi for use of his ranch for this
research. The author’s also thanks to the subject editor and the
three anonymous reviewers for their constructive comments.
J. Curiel Yuste finally thanks Prof. Josep Penuelas and Dr.
Roberto Molowny–Horas for their help and comments.
Open Access This article is distributed under the terms of the
Creative Commons Attribution Noncommercial License which
permits any noncommercial use, distribution, and reproduction
in any medium, provided the original author(s) and source are
credited.
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